一个大洋环流模式中赤道西太平洋暖池海面温度对西风爆发的响应

用逐日的欧洲中期数值预报中心再分析(ERA)风应力,和由Haney公式结合ERA海表资料与预报海温计算出的热通量强迫一个全球大洋环流模式.并用逐日的模拟结果与TOGA-COARE(Tropical Ocean--Global Atmosphere--Coupled Ocean-AtmosphereResponse Experiment)浮标观测资料对比,分析模拟结果中暖池海区上层海洋热量平衡对西风爆发(WWB)的响应.在第一次WWB过程中,模拟与观测的主要差异在WWB期间,而造成差异的原因主要是模式中由下沉运动引起的增温和由强的纬向温度梯度引起的暖平流.初步认为下沉增温可能是差分格式本身和模式分辨率不足造成的.从热量平衡的结果看,第二次WWB事件的模拟比第一次更成功,两次差异可能与两次WWB事件的季节背景不同有关.     

西风爆发时赤道西太平洋热量平衡的诊断分析

西风爆发(WWB)对赤道西太平洋暖池SST的影响是WWB与ENSO相互作用的一个重要环节。作者运用TOGA(TropicalOcean-GlobalAtmosphere)-COARE(CoupledOcean-AtmosphereResponseExperiment)期间暖池海区8个浮标的观测资料，根 据Stenven和Niiler的热量平衡诊断方法，从能量平衡角度分析了赤道156°E附近SST对1992年9月到1993年1月期间两次WWB事件的响应。两次WWB期间，SST都是在 WWB之前和其后升温，而在WWB期间降温，升温和降温的幅度均为1℃左右。热量平衡分析结果表明，季节内时间尺度上海表热通量是导致赤道西太平洋暖池混合层温度变化的主要因素。在海表热通量中，短波辐射和潜热是变化量最大的两项，决定着净通量的正负，其他项的变化则相对较小。水平平流项有时可以超过海表热通量的作用，其作用不可忽略。两次WWB的过程中，季节变化造成的混合层的温盐结构和流场结构的差异是导致两次WWB事件时上层海洋热量平衡差别的主要因素。另外，混合层的厚度也有明显的变化，而混合层厚度变化的差异则是与流场的结构以及WWB的向东传播特征密切相关。     

北京一次罕见夜间突发性强增温事件成因分析

夜间降温是正常的地表气温日变化,但统计表明北京及周边地区冬半年时常出现入夜后气温不降反升的现象,甚至出现了小时升温超过10℃的剧烈增温事件。这种增温具有明显的突发性,且很快转为降温,常对业务预报造成困扰。2010年11月26日夜间冷锋过境该区域造成了最强达12℃·h^-1的夜间急剧增温事件,与气候统计结果相比,该次增温幅度和影响区域范围非常罕见且极端。文章对其进行了详细诊断分析,使用的资料包括自动气象站、常规地面和探空、铁塔、卫星、风廓线雷达等观测资料和美国环境预报中心(NCEP)最终分析资料。分析结果表明:该次过程对流层低层有非常显著的冷平流;垂直速度诊断、卫星和风廓线雷达观测都表明,对流层中低层都存在显著的强下沉运动;铁塔观测和北京探空观测演变都表明增温过程中近地面大气有显著的湍流运动。分析此次罕见过程的机理包括三个方面:高原地区地面位温显著高于平原地区(二者最大可差10 K),是该次罕见增温事件形成的首要条件;强下沉运动使得低密度高位温空气强迫下沉到边界层,是增温必要条件;强湍流混合作用则是地面空气增温的必要机制。估算结果表明,边界层急流伴随的强湍流混合可引起约8℃的罕见地面空气增温。最后,给出了该次事件的机理概念模型。     

西安东部夜间异常增温原因及其数值模式预报订正

利用陕西省气象监测站观测资料、NCEP/NCAR和ERA5全球再分析资料,对2019年12月10日凌晨西安东部一次夜间温度异常跃增过程进行深入分析.结果表明:①此次夜间增温过程发展迅速、局地性强、预报难度大.②500 hPa内蒙东部冷槽底后部干燥的西北气流和850 hPa干暖气团有利于产生大气下沉绝热增温.西安地区近地...     

土壤湿度的一种统计预报模型初步试验

探讨土壤湿度模拟与预报的可能途径，本文从水量平衡的物理原理出发，利用统计模型建立完全预报方程与简化预报方程，以淮河流域能量与水循环试验(HUBEX)的外场观测试验区所得淮河史灌河流域土壤水分的加密观测资料及其同期降水、蒸发、径流等水文观测资料(逐日资料)为例，进行了预报试验。结果表明，预报精度约为87．3％-88．3％。可见，利用前一日和前两日的降水和土壤湿度可以尝试作为估计未来土壤湿度的预报因子，但这只是一次初步的试验。     

基于新型探测资料对西安一次弱降水预报失误的原因分析

利用微波辐射计、激光测风雷达、多普勒雷达、相控阵雷达等新型探测资料、地面加密观测资料、ERA5再分析及多模式数值预报结果对2022年4月24日西安城区一次弱降水预报出现明显失误的原因进行分析,结果表明：(1)全球数值预报模式和中尺度数值预报模式对本次过程西安城区均报有明显降水,主要原因为模式对低层相对湿度预报明显偏大;(2)多种新型探测数据分析认为近地层湿度条件较差及中层的绝对水汽含量低,中层的干暖空气不利于成云致雨,垂直上升运动不强,且由于低层非常干燥,使水滴在下沉过程中蒸发,从而无法形成雨滴下落,这些原因共同造成西安城区无降水,低层相对湿度预报偏大是造成这次西安城区降水预报失误的主要原因之一;(3)造成西安城区近地层湿度条件差的原因是城市干热岛效应和低层干暖平流输送,且降水云团翻越秦岭后其湿空气绝热下沉至城区后出现增温降湿,使得城区形成较为深厚的干层,即使有雨滴在下落过程中也会造成更强的蒸发,这也是城区没有降水的重要原因之一;(4)预报员主观预报订正出现空报主要是源于对边界层水汽、抬升条件等关键降水要素缺乏订正能力,且对大城市的干热岛效应和秦岭山区地形影响研究不足。     

BDA方案对台风背景高温天气预报的改进

广州地Ⅸ的高温天气主要是受副热带高压和台风外围下沉气流的影响所致.文中采用BDA(Bogus Data Assimila-tion)方法,探讨BDA方案对广州地区台风背景条件下高温预报的改进能力.选取2005年7月中旬广州地区出现的高温天气进行研究.这是比较典型的受副热带高压和台风(海棠)共同影响造成高温的天气过程.分析有无采用BDA方案的模式初始场.结果表明:采用BDA方案同化Bogus模型可以调整台风中心位置和强度,使所得到的初始场中心位置与观测更为接近,台风强度(气压梯度力、风速)比末用Bogus的情况强,与观测值更为接近.数值模拟的结果表明,采用了BDA方案的敏感试验可以更好地预撤台风路径和台风中心强度变化,从而更好地预报高温天气,对高温区分布、日平均温度大小等的预报都有改进.文中对引起这种预报差异的原因进行了讨论,并探讨高温预报改进的可能机制.大气下沉运动的增强是高温预报改进的主要原因.敏感试验由于广州中低层大气的水汽减少,大气的下沉增强,致使天空的云量减少,对太阳短波辐射的阻挡减小,从而地面吸收热量增多,温度升高,输送给大气的感热增加,大气气温升高.采用BDA方案可以改进模式在台风"海棠"过程对广州高温的预报.     

CMA-GFS全球模式海洋边界层高度的主要偏差特征

海洋边界层高度是表征海洋上空大气的水汽、热量、物质等垂直分布的重要特征量，同时在气候、污染、模式预报上有关键作用。然而，利用海洋边界层高度观测对数值天气预报模式进行诊断的研究很少。因此，本文利用2019—2020年GPS掩星资料计算出的海洋边界层高度的分布特征，对CMA GFS全球模式的预报性能进行分析，同时借助ERA5再分析资料对CMA GFS模式的偏差进行讨论。主要结论如下：①CMA GFS全球模式在西太平洋、南太平洋、南大西洋绝大部分海域预报的边界层高度比较合理;②模式在热带辐合带海域和南太平洋辐合带存在高估预报，初步分析与模式对热带深对流的抬升凝结高度的预报偏高有关。③模式和ERA5在南半球层积云所在区域均存在边界层高度预报偏低，初步分析可能是模式对南半球层积云顶辐射冷却驱动的湍流扩散偏小造成。④模式在有云的大气下主要呈现为预报偏高，中心值在200 m左右，而在晴空区域模式预报较为合理，偏差值范围较小，ERA5也存在类似的特点。     

IAP AGCM模式对地面气温长期趋势的预报分析

分析了1979～2004年共26年中国台站地面气温的观测资料以及中国科学院大气物理研究所的大气环流模式IAP AGCM在夏季和冬季的季节集合预报结果对地面气温长期变化趋势的预报.分析结果表明,在所研究的26年中,中国大部分地区是一个增温的趋势,其中冬季增温比夏季增温显著.IAP AGCM集合预报不能很好地预报出观测资料...     

1960-2005年长江流域降水极值概率分布特征

 摘 要：根据1960-2005年长江流域147个气象站逐日降水观测资料和ECHAM5/ MPI-OM气候模式20世纪试验期（1941-2000年）79个格点逐日降水模拟资料，建立年最大强降水AM（annual maximum）序列及汛期日降水量＜1.27 mm的最长干旱持续天数MI（Munger index）序列，分析了长江流域降水极值序列的时空分布特征和概率分布模式。结果表明：1) 长江流域强降水事件的强度和概率最大的地区位于岷沱江流域中游、洞庭湖湖区、长江中下游干流区与鄱阳湖东南部支流等地区，干旱事件强度和概率最大的地区位于金沙江流域中下游与嘉陵江流域；2) 气候模式模拟的长江流域AM事件的多年平均值普遍高于观测值，但离差系数普遍低于观测值; 3) 气候模式模拟结果与观测的降水极值空间分布有一定的差异，但对气候模式和实际观测的降水极值概率分布的拟合，均证明Wakeby分布函数能够较好地拟合降水极值的概率分布。     

CHARACTERISTICS AND TRENDS OF CLIMATIC EXTREMES IN CHINA DURING 1959–2014

The spatial and temporal variations of daily maximum temperature(Tmax), daily minimum temperature(Tmin), daily maximum precipitation(Pmax) and daily maximum wind speed(WSmax) were examined in China using Mann-Kendall test and linear regression method. The results indicated that for China as a whole, Tmax, Tmin and Pmax had significant increasing trends at rates of 0.15℃ per decade, 0.45℃ per decade and 0.58 mm per decade,respectively, while WSmax had decreased significantly at 1.18 m·s~(-1) per decade during 1959—2014. In all regions of China, Tmin increased and WSmax decreased significantly. Spatially, Tmax increased significantly at most of the stations in South China(SC), northwestern North China(NC), northeastern Northeast China(NEC), eastern Northwest China(NWC) and eastern Southwest China(SWC), and the increasing trends were significant in NC, SC, NWC and SWC on the regional average. Tmin increased significantly at most of the stations in China, with notable increase in NEC, northern and southeastern NC and northwestern and eastern NWC. Pmax showed no significant trend at most of the stations in China, and on the regional average it decreased significantly in NC but increased in SC, NWC and the mid-lower Yangtze River valley(YR). WSmax decreased significantly at the vast majority of stations in China, with remarkable decrease in northern NC, northern and central YR, central and southern SC and in parts of central NEC and western NWC. With global climate change and rapidly economic development, China has become more vulnerable to climatic extremes and meteorological disasters, so more strategies of mitigation and/or adaptation of climatic extremes,such as environmentally-friendly and low-cost energy production systems and the enhancement of engineering defense measures are necessary for government and social publics.     

Could the Recent Taal Volcano Eruption Trigger an El Ni?o and Lead to Eurasian Warming?

正The Taal Volcano in Luzon is one of the most active and dangerous volcanoes of the Philippines. A recent eruption occurred on 12 January 2020(Fig. 1a), and this volcano is still active with the occurrence of volcanic earthquakes. The eruption has become a deep concern worldwide, not only for its damage on local society, but also for potential hazardous consequences on the Earth's climate and environment.     

ANALYSIS OF “BIMODAL PATTERN” STORM ACTIVITY CHARACTERISTICS OF THE BAY OF BENGAL

Storms that occur at the Bay of Bengal (BoB) are of a bimodal pattern, which is different from that of the other sea areas. By using the NCEP, SST and JTWC data, the causes of the bimodal pattern storm activity of the BoB are diagnosed and analyzed in this paper. The result shows that the seasonal variation of general atmosphere circulation in East Asia has a regulating and controlling impact on the BoB storm activity, and the “bimodal period” of the storm activity corresponds exactly to the seasonal conversion period of atmospheric circulation. The minor wind speed of shear spring and autumn contributed to the storm, which was a crucial factor for the generation and occurrence of the “bimodal pattern” storm activity in the BoB. The analysis on sea surface temperature (SST) shows that the SSTs of all the year around in the BoB area meet the conditions required for the generation of tropical cyclones (TCs). However, the SSTs in the central area of the bay are higher than that of the surrounding areas in spring and autumn, which facilitates the occurrence of a “two-peak” storm activity pattern. The genesis potential index (GPI) quantifies and reflects the environmental conditions for the generation of the BoB storms. For GPI, the intense low-level vortex disturbance in the troposphere and high-humidity atmosphere are the sufficient conditions for storms, while large maximum wind velocity of the ground vortex radius and small vertical wind shear are the necessary conditions of storms.     

LOW-FREQUENCY CIRCULATION AND EXTENDED-RANGE FORECAST IN CONNECTION WITH AUTUMN EXTREME HEAVY RAINFALL PROCESS IN HAINAN

Observed daily precipitation data from the National Meteorological Observatory in Hainan province and daily data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis-2 dataset from 1981 to 2014 are used to analyze the relationship between Hainan extreme heavy rainfall processes in autumn (referred to as EHRPs) and 10–30 d low-frequency circulation. Based on the key low-frequency signals and the NCEP Climate Forecast System Version 2 (CFSv2) model forecasting products, a dynamical-statistical method is established for the extended-range forecast of EHRPs. The results suggest that EHRPs have a close relationship with the 10–30 d low-frequency oscillation of 850 hPa zonal wind over Hainan Island and to its north, and that they basically occur during the trough phase of the low-frequency oscillation of zonal wind. The latitudinal propagation of the low-frequency wave train in the middle-high latitudes and the meridional propagation of the low-frequency wave train along the coast of East Asia contribute to the ‘north high (cold), south low (warm)’ pattern near Hainan Island, which results in the zonal wind over Hainan Island and to its north reaching its trough, consequently leading to EHRPs. Considering the link between low-frequency circulation and EHRPs, a low-frequency wave train index (LWTI) is defined and adopted to forecast EHRPs by using NCEP CFSv2 forecasting products. EHRPs are predicted to occur during peak phases of LWTI with value larger than 1 for three or more consecutive forecast days. Hindcast experiments for EHRPs in 2015–2016 indicate that EHRPs can be predicted 8–24 d in advance, with an average period of validity of 16.7 d.     

AN ATTRIBUTION ANALYSIS OF CHANGES IN POTENTIAL EVAPOTRANSPIRATION IN THE BEIJING–TIANJIN–HEBEI REGION UNDER CLIMATE CHANGE

Based on the measurements obtained at 64 national meteorological stations in the Beijing–Tianjin–Hebei (BTH) region between 1970 and 2013, the potential evapotranspiration (ET0) in this region was estimated using the Penman–Monteith equation and its sensitivity to maximum temperature (Tmax), minimum temperature (Tmin), wind speed (Vw), net radiation (Rn) and water vapor pressure (Pwv) was analyzed, respectively. The results are shown as follows. (1) The climatic elements in the BTH region underwent significant changes in the study period. Vw and Rn decreased significantly, whereas Tmin, Tmax and Pwv increased considerably. (2) In the BTH region, ET0 also exhibited a significant decreasing trend, and the sensitivity of ET0 to the climatic elements exhibited seasonal characteristics. Of all the climatic elements, ET0 was most sensitive to Pwv in the fall and winter and Rn in the spring and summer. On the annual scale, ET0 was most sensitive to Pwv, followed by Rn, Vw, Tmax and Tmin. In addition, the sensitivity coefficient of ET0 with respect to Pwv had a negative value for all the areas, indicating that increases in Pwv can prevent ET0 from increasing. (3) The sensitivity of ET0 to Tmin and Tmax was significantly lower than its sensitivity to other climatic elements. However, increases in temperature can lead to changes in Pwv and Rn. The temperature should be considered the key intrinsic climatic element that has caused the "evaporation paradox" phenomenon in the BTH region.     

Revisiting the Concentration Observations and Source Apportionment of Atmospheric Ammonia

正While China’s Air Pollution Prevention and Control Action Plan on particulate matter since 2013 has reduced sulfate significantly, aerosol ammonium nitrate remains high in East China. As the high nitrate abundances are strongly linked with ammonia, reducing ammonia emissions is becoming increasingly important to improve the air quality of China. Although satellite data provide evidence of substantial increases in atmospheric ammonia concentrations over major agricultural regions, long-term surface observation of ammonia concentrations are sparse. In addition, there is still no consensus on     

COMPARATIVE ANALYSIS OF VARIOUS DATA OF AIR–SEA TEMPERATURE DIFFERENCE AND ITS VARIATION ACROSS SOUTH CHINA SEA IN THE PAST 35 YEARS

Using the International Comprehensive Ocean-Atmosphere Data Set(ICOADS) and ERA-Interim data, spatial distributions of air-sea temperature difference(ASTD) in the South China Sea(SCS) for the past 35 years are compared,and variations of spatial and temporal distributions of ASTD in this region are addressed using empirical orthogonal function decomposition and wavelet analysis methods. The results indicate that both ICOADS and ERA-Interim data can reflect actual distribution characteristics of ASTD in the SCS, but values of ASTD from the ERA-Interim data are smaller than those of the ICOADS data in the same region. In addition, the ASTD characteristics from the ERA-Interim data are not obvious inshore. A seesaw-type, north-south distribution of ASTD is dominant in the SCS; i.e., a positive peak in the south is associated with a negative peak in the north in November, and a negative peak in the south is accompanied by a positive peak in the north during April and May. Interannual ASTD variations in summer or autumn are decreasing. There is a seesaw-type distribution of ASTD between Beibu Bay and most of the SCS in summer, and the center of large values is in the Nansha Islands area in autumn. The ASTD in the SCS has a strong quasi-3a oscillation period in all seasons, and a quasi-11 a period in winter and spring. The ASTD is positively correlated with the Nio3.4 index in summer and autumn but negatively correlated in spring and winter.     

Analysis of Atmospheric Boundary Layer Height Characteristics over the Arctic Ocean Using the Aircraft and GPS Soundings

正ERRATUM to: Atmospheric and Oceanic Science Letters, 4(2011), 124-130 On page 126 of the printed edition (Issue 2, Volume 4), Fig. 2 was a wrong figure because the contact author made mistake giving the wrong one. The corrected edition has been updated on our website. The editorial office is sincerely sorry for any     

Index to Vol.31

正AN Junling;see LI Ying et al.;(5),1221—1232AN Junling;see QU Yu et al.;(4),787-800AN Junling;see WANG Feng et al.;(6),1331-1342Ania POLOMSKA-HARLICK;see Jieshun ZHU et al.;(4),743-754Baek-Min KIM;see Seong-Joong KIM et al.;(4),863-878BAI Tao;see LI Gang et al.;(1),66-84BAO Qing;see YANG Jing et al.;(5),1147—1156BEI Naifang;